Data at Work

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Monitoring, Streamlining and Reorganizing Work with Digital Technology

A case study on software for process mining, workflow automation, algorithmic management and AI based on rich behavioral data about workers.

Wolfie Christl, Cracked Labs, September 2023

This case study is part of the ongoing project “Surveillance and Digital Control at Work” (2023-2024) led by Cracked Labs, which aims to explore how companies use personal data on workers in Europe, together with AlgorithmWatch, Jeremias Prassl (Oxford), UNI Europa and GPA, funded by the Austrian Arbeiterkammer.


Data collection in the workplace has become ubiquitous. Employers use a growing number of information systems to plan, organize and manage workflows and work performed by their employees, most prominently systems for enterprise resource planning (ERP) and customer relationship management (CRM), which are now used by mid- to large-size organizations in most industries. Many systems constantly store digital records about work activities and behaviors of employees. This data is increasingly stored in centralized databases and in the cloud. Employers exploit the data to support managerial decisions, organize work, automate workflows and monitor workers. The technical systems in place are often complex and opaque. Most workers will not be aware of the data flows and decisions that occur in the background while they routinely interact with networked software and devices at work.

This case study explores, examines and documents software systems and technologies used by employers that utilize extensive personal data about the work activities and behaviors of employees to streamline, reorganize and manage work, expand control over workers, subject them to digital monitoring and make automated decisions about them – with a focus on Europe. To illustrate wider practices, it investigates cloud-based software for enterprise data analytics, workflow automation and algorithmic management provided by the German vendor Celonis, based on a detailed analysis of software documentation and other corporate sources.

Celonis is considered the global market leader in software for process mining, which utilizes activity log data recorded by enterprise systems from vendors like SAP, Oracle, Salesforce and Microsoft to create a digital representation of how work is actually being performed in an organization, down to granular steps and tasks. Process mining aims to analyze, standardize and optimize workflows in order to make them more productive and efficient while lowering costs. Still considered a “startup”, Celonis has a significant customer base in Europe and the US. It received more than a billion in venture capital and was listed among the five largest private investments in “AI” technology globally in 2022. SAP started reselling its technology in 2015. Since then, Celonis has added functionality for workflow automation and task management and started to refer to its technology as an “execution management system”, putting the focus on managing rather than merely analyzing work. Several consulting firms like KPMG, Deloitte, Accenture, Capgemini, IBM and the Porsche subsidiary MHP provide Celonis-based applications.

This case study documents a wide range of data practices, which can affect workers in many fields, from insurance claim handling to manufacturing, from creative work to warehouse picking, from low-wage to knowledge work:

  • Analyzing extensive personal data. Celonis analyzes large amounts of log data about work activities recorded by ERP, CRM and other enterprise software systems. This can occur in real time and include millions of time-stamped activity records that typically contain personal data about the workers who perform the activities.
  • Streamlining, reorganizing and managing work. Based on the data, Celonis evaluates, assesses and monitors workflows in many industries in order to optimize them in line with the employers’ business goals. Metrics about productivity, time, quality, automation and cost are ubiquitous. Several mechanisms help to automate the reorganization and management of work. The “process AI” promises to identify the “root causes” for “undesired activities” and other inefficiencies. The system can also notify managers of deviations from KPI targets and assign them tasks. The “simulation” module forecasts and predicts the impact of process changes.
  • Group-level digital control. The analysis of workflows, activities and metrics for groups, such as teams, departments, units, offices, plants or subcontractors, plays a major role. The system lets employers drill down into group data and compare different groups. Group-level analysis can facilitate internal competition and represents a form of performance monitoring for managers, who are expected to pass the pressure on to workers. It can also facilitate peer control, where members of a team or other groups put pressure on each other.
  • Granular performance and behavior monitoring. Celonis’ technology can be used to scrutinize work at the level of individual employees and monitor, rate and rank named workers by their productivity, speed, work outcomes and behaviors. Employers can use it for granular performance and behavior monitoring, from rating what call center agents say in conversations to assessing tasks down to the second in manufacturing.
  • Analyzing social interactions. Another software module “adds the social aspect of processes” to the system and promises to assess work activities with respect to social interactions and collaboration between workers.
  • Workflow automation across enterprise systems. In addition, the system can facilitate workflows and real-time data sharing across Celonis’ process mining software and hundreds of other enterprise systems, such as for ERP, CRM, HRM, task management and communication. It can automatically initiate particular actions in SAP, Salesforce, Workday or Microsoft 365 when certain criteria are met in the process data. It can also trigger actions based on networked access to other enterprise systems, for example, by watching the location of a delivery driver or by monitoring the corporate chat system Slack or an Outlook email inbox.
  • Automated task assignment. Celonis’ workflow automation technology can involve processing workers’ personal data and making automated decisions about them. It can automatically prioritize, distribute and assign tasks to workers and provide them with a limited set of recommended actions to perform.
  • Apps that combine process analysis and algorithmic management. Employers, consulting firms and other vendors can create Celonis-based applications that address particular processes in an organization. These apps can combine process analysis, management automation, workflow automation and task assignment.
  • Recording screen, application, browser, keyboard and mouse activity. Another Celonis technology analyzes interactions, behaviors and activities performed on the desktop computers of employees. The “task mining” system can capture extensive personal data including screen recordings, keystrokes, mouse clicks and clipboard contents from up to 2,500 employees. While employers can customize the captured data, example applications show that the system can be used to scrutinize how workers use programs and applications, websites, keyboard commands and copy/paste functionality. Employers can combine data on desktop interactions with activity data from enterprise systems and use it to detect “inefficiencies”, decrease the time spent on “non-value adding activities” and assess “workforce productivity”, “productive time” and “idle time”. Corporate sources suggest that analysis results can be displayed both at the level of teams and for individual workers.

Employers can customize the systems offered by Celonis and its partners, use only parts of them or use them in less intrusive ways. The last section of the case study summarizes the identified data practices and discusses potential implications for workers. While granular performance monitoring at the individual level is clearly problematic, extracting aggregate knowledge from personal data increases the power imbalance at work and can also have significant effects. Utilizing the data to standardize and unilaterally reorganize workflows can accelerate and intensify work, reduce discretion, make workers easier to replace, facilitate outsourcing, undermine bargaining power and affect wages. Employers may refer to “objective” data to justify arbitrary decisions. Automated task assignment and algorithmic management practices can also have a variety of side effects. The rapid expansion of data flows and functionality potentially undermines purpose limitation, a cornerstone of European data protection law.

The findings of this case study will be incorporated in the main report of the ongoing project “Surveillance and Digital Control at Work” (2023-2024), led by Cracked Labs, which aims to explore how companies use personal data on workers in Europe. The main report will draw further conclusions.